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Kazemi, A., Mahmoudi, A., Khojastehnazhand, M., Fattahi, S. (2024). Application of FT-IR Spectroscopy with Various Classification and Regression Models for Detection and Quantification of Sodium Hydrosulfite in Iranian Wheat Flour. , 20(3), 17-31. doi: 10.22067/ifstrj.2024.86585.1312
Amir Kazemi; Asghar Mahmoudi; Mostafa Khojastehnazhand; Seyyed Hossein Fattahi. "Application of FT-IR Spectroscopy with Various Classification and Regression Models for Detection and Quantification of Sodium Hydrosulfite in Iranian Wheat Flour". , 20, 3, 2024, 17-31. doi: 10.22067/ifstrj.2024.86585.1312
Kazemi, A., Mahmoudi, A., Khojastehnazhand, M., Fattahi, S. (2024). 'Application of FT-IR Spectroscopy with Various Classification and Regression Models for Detection and Quantification of Sodium Hydrosulfite in Iranian Wheat Flour', , 20(3), pp. 17-31. doi: 10.22067/ifstrj.2024.86585.1312
Kazemi, A., Mahmoudi, A., Khojastehnazhand, M., Fattahi, S. Application of FT-IR Spectroscopy with Various Classification and Regression Models for Detection and Quantification of Sodium Hydrosulfite in Iranian Wheat Flour. , 2024; 20(3): 17-31. doi: 10.22067/ifstrj.2024.86585.1312

Application of FT-IR Spectroscopy with Various Classification and Regression Models for Detection and Quantification of Sodium Hydrosulfite in Iranian Wheat Flour

مجله پژوهشهای علوم و صنایع غذایی ایران
Article 2, Volume 20, Issue 3 - Serial Number 87, July and August 2024, Pages 17-31    PDF (1008.77 K)
Document Type: Research Article-en
DOI: 10.22067/ifstrj.2024.86585.1312
Authors
Amir Kazemi* 1; Asghar Mahmoudi1; Mostafa Khojastehnazhand2; Seyyed Hossein Fattahi3
1Department of Biosystems Engineering, University of Tabriz, Tabriz, Iran
2Department of Mechanical Engineering, University of Bonab, Bonab, Iran
3Department of Biosystems Engineering, University of Maragheh, Maragheh, Iran
Abstract
Wheat flour is one of the most important and strategic food resources especially in developing countries. The addition of Sodium hydrosulfite to flour for improving some appearance features can have dangerous impacts on the consumer health. Therefore, detection of this harmful substance is great practical significance. In the present study, the potential of Fourier transform-mid infrared (FT-MIR) spectroscopy in 400-4000 cm-1 for the fast detection of Sodium hydrosulfite powder in wheat flour was investigated. After getting the spectral data from samples, firstly some preprocessing methods were used to correct harmful and unwanted effects on spectral data, and then Principal Component Analysis (PCA) as unsupervised and Support Vector Machine (SVM) and Artificial Neural Network (ANN) models as supervised classification models and Partial Least Square Regression (PLSR) as regression model were applied to detect and quantify the adulteration in pure flour samples. The best outcomes were the accuracy of 86.66 and 86.70 for SVM and ANN models with S-G + D2 + SNV preprocessing, respectively and R2p = 0.99 For PLSR model.
Keywords
Adulteration; Chemometrics; Sodium hydrosulfite; Spectroscopy; Wheat flour
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